Location of subsurface targets in geophysical data using neural networks

Neural networks were used to estimate the offset, depth, and conductivity-area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. For input patterns with less than 100 elements, the directed random search and functional line networks gave the best results. For patterns with more than 100 elements, self-organizing map to back propagation was most accurate. Using the whole ellipticity image gave the most accurate results for all the network paradigms. -from Authors